import sys
# Install gurobi
!{sys.executable} -m pip install -i https://pypi.gurobi.com gurobipy
!{sys.executable} -m pip install plotly geopy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product

import gurobipy as gp
from gurobipy import GRB, quicksum

# These are ours
import problem
import visualization

Looking in indexes: https://pypi.gurobi.com
Requirement already satisfied: gurobipy in /opt/anaconda3/lib/python3.8/site-packages (9.1.1)
Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.8/site-packages (4.14.3)
Requirement already satisfied: geopy in /opt/anaconda3/lib/python3.8/site-packages (2.1.0)
Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.3.3)
Requirement already satisfied: geographiclib<2,>=1.49 in /opt/anaconda3/lib/python3.8/site-packages (from geopy) (1.50)

def normalize(i, j):
    assert i != j
    return (i, j) if i < j else (j, i)

Problem configuration

from model import Autonomax, Config

# Run on the first |C| cities (mostly for testing)
C = 41

# What scenario we will be utilizing
scenario = 0
# The number of cities in the core net
NC = 4
# 1 if the core net should be a cycle; 0 if it shoul be a path.
Z = 1

autonomax = Autonomax(
    Config(
        cities=problem.cities[:C],
        distances=problem.D[:C, :C],
        demand=problem.B[scenario][:C],
        core_city_count=NC,
        core_net_is_cycle=Z,
    )
)

Academic license - for non-commercial use only - expires 2021-05-15
Using license file /Users/sjurwold/gurobi.lic

A = autonomax.model.getA()

count = lambda d: len(d) if isinstance(d, gp.tupledict) else 1

V = sum(count(v) for v in autonomax.variables.values())
C = sum(count(c) for c in autonomax.constraints.values())

print(f'V = {V}, C = {C}')

fig, ax = plt.subplots(1, figsize=(128, 128 * C/V))
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.spy(A)


total = 0
print(f'\nCONSTRAINT SETS:')
for (name, constraint) in autonomax.constraints.items():
    constraints_in_set = count(constraint)
    print(f'{constraints_in_set:>5} | {name}')
    #plt.gcf().text(0.07, 0.69 - 0.4 * (total + 0.5 * count(constraint)) / C, name, fontsize=14)
    total += constraints_in_set
    plt.plot([0, V], [total - 0.5, total - 0.5], color='grey')
    
total = 0
print(f'\nVARIABLE SETS:')
for (name, variables) in autonomax.variables.items():
    variables_in_set = count(variables)
    print(f'{variables_in_set:>5} | {name}')
    #plt.gcf().text(0.1 + 0.8 * (total + 0.5*count(variables)) / V, 0.8, name, fontsize=14)
    total += count(variables)
    plt.plot([total - 0.5, total - 0.5], [0, C], color='grey')
    
plt.savefig('constraint-matrix.png', dpi=200)

V = 9389, C = 6153

CONSTRAINT SETS:
    1 | one_control_center
   41 | control_city_directly_connected
 1681 | reduce_control_center_symmetry
   41 | core_city_ub
    1 | cycle_or_path
   41 | disallow_core_tree
    1 | exactly_nc_core_cities
   41 | control_center_is_connected
 2460 | is_connectable
  123 | is_connected_timestep
   41 | connected_graph
   41 | conservation_of_flow
  820 | force_edge_if_flow
  820 | edge_cost_lb

VARIABLE SETS:
   41 | is_control_center
  820 | is_core_edge
   41 | is_core_city
  164 | is_connected_step
 5043 | is_connectable_step
  820 | flow
  820 | abs_flow
  820 | is_sub_edge
  820 | edge_cost

autonomax.model.Params.timeLimit = 600.0
autonomax.model.optimize()

Changed value of parameter timeLimit to 600.0
   Prev: inf  Min: 0.0  Max: inf  Default: inf
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 6153 rows, 9389 columns and 30299 nonzeros
Model fingerprint: 0xc49bf554
Model has 820 general constraints
Variable types: 2460 continuous, 6929 integer (6929 binary)
Coefficient statistics:
  Matrix range     [1e+00, 2e+05]
  Objective range  [1e+00, 2e+04]
  Bounds range     [1e+00, 1e+02]
  RHS range        [7e-01, 4e+01]
Presolve removed 93 rows and 2633 columns
Presolve time: 0.09s
Presolved: 6060 rows, 6756 columns, 30309 nonzeros
Variable types: 2460 continuous, 4296 integer (4296 binary)
Found heuristic solution: objective 88012.481927

Root relaxation: objective 1.531060e+03, 5203 iterations, 0.13 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 1531.06029    0  148 88012.4819 1531.06029  98.3%     -    0s
H    0     0                    56219.378601 1531.06029  97.3%     -    0s
H    0     0                    26291.282650 2035.67488  92.3%     -    0s
     0     0 2496.88681    0  247 26291.2827 2496.88681  90.5%     -    0s
H    0     0                    22932.121931 2496.88681  89.1%     -    0s
     0     0 3195.95627    0  213 22932.1219 3195.95627  86.1%     -    0s
     0     0 3195.97935    0  208 22932.1219 3195.97935  86.1%     -    0s
     0     0 3352.66847    0  322 22932.1219 3352.66847  85.4%     -    0s
     0     0 3361.96333    0  316 22932.1219 3361.96333  85.3%     -    0s
     0     0 3367.26315    0  351 22932.1219 3367.26315  85.3%     -    0s
     0     0 3368.00607    0  351 22932.1219 3368.00607  85.3%     -    0s
     0     0 3368.01742    0  352 22932.1219 3368.01742  85.3%     -    0s
     0     0 3371.24076    0  407 22932.1219 3371.24076  85.3%     -    1s
     0     0 3376.32460    0  349 22932.1219 3376.32460  85.3%     -    1s
     0     0 3376.41830    0  329 22932.1219 3376.41830  85.3%     -    1s
     0     0 3385.28766    0  322 22932.1219 3385.28766  85.2%     -    1s
     0     0 3393.25321    0  307 22932.1219 3393.25321  85.2%     -    1s
     0     0 3399.55904    0  328 22932.1219 3399.55904  85.2%     -    1s
     0     0 3399.77551    0  332 22932.1219 3399.77551  85.2%     -    1s
     0     0 3400.81526    0  318 22932.1219 3400.81526  85.2%     -    1s
     0     0 3400.86624    0  300 22932.1219 3400.86624  85.2%     -    1s
     0     0 3401.38822    0  371 22932.1219 3401.38822  85.2%     -    1s
     0     0 3401.38920    0  362 22932.1219 3401.38920  85.2%     -    1s
H    0     0                    20458.793698 3401.38920  83.4%     -    1s
     0     0 3401.86941    0  303 20458.7937 3401.86941  83.4%     -    1s
     0     0 3401.86941    0  265 20458.7937 3401.86941  83.4%     -    2s
H    0     0                    19869.314167 3401.86941  82.9%     -    2s
     0     2 3401.89759    0  252 19869.3142 3401.89759  82.9%     -    2s
H   31    40                    19211.749615 3581.29864  81.4%   523    3s
H   32    40                    18446.066483 3581.29864  80.6%   511    3s
H   71    80                    17486.992036 3581.29864  79.5%   349    3s
H   72    80                    17394.029414 3581.29864  79.4%   346    3s
H  112   119                    17260.979930 3581.29864  79.3%   284    4s
H  112   119                    17215.667895 3581.29864  79.2%   284    4s
H  115   119                    17026.054430 3581.29864  79.0%   281    4s
H  167   172                    16966.965510 3581.29864  78.9%   226    4s
H  172   172                    16953.979724 3581.29864  78.9%   224    4s
H  173   172                    16934.095273 3581.29864  78.9%   222    4s
H  395   373                    16895.063284 3581.29864  78.8%   137    5s
H  396   373                    16840.552031 3581.29864  78.7%   137    5s
H  400   373                    16779.533948 3581.29864  78.7%   137    5s
*  830   789             127    16689.915461 3743.37268  77.6%  92.3    6s
H  895   773                    16064.503392 3743.85872  76.7%  91.5    6s
H 1035   905                    16062.488194 3743.85872  76.7%  89.1    6s
H 1040   905                    16053.351895 3743.85872  76.7%  89.1    6s
H 1044   904                    15898.838430 3743.85872  76.5%  89.8    6s
  1553  1323 8672.84706   19  220 15898.8384 3743.85872  76.5%  83.4   10s
H 1564  1263                    15895.048881 3743.85872  76.4%  82.8   13s
  1571  1268 13560.2623  136  304 15895.0489 3743.85872  76.4%  82.4   15s
  1576  1275 3743.85872   15  276 15895.0489 3743.85872  76.4%   103   20s
  1619  1299 7619.72284   19  407 15895.0489 4054.38721  74.5%   116   25s
  1808  1354 8423.55555   28  241 15895.0489 4054.38721  74.5%   137   30s
H 1873  1314                    15895.048877 4054.38721  74.5%   138   31s
H 1932  1263                    15895.048851 4054.38721  74.5%   141   32s
H 1935  1205                    15895.048758 4054.38721  74.5%   141   32s
H 2005  1156                    15895.048689 4054.38721  74.5%   142   33s
H 2011  1102                    15895.048684 4054.38721  74.5%   143   33s
  2165  1127 7760.50094   44  189 15895.0487 4054.38721  74.5%   144   35s
H 2299  1052                    15895.048611 4054.38721  74.5%   147   35s
H 2578  1045                    15895.048560 5713.87382  64.1%   153   38s
  2860  1068     cutoff   24      15895.0486 6333.75127  60.2%   157   40s
  3496  1183 9327.45633   32  193 15895.0486 6876.79526  56.7%   160   45s
H 3688  1243                    15895.048505 7005.60093  55.9%   161   46s
  4159  1382 14892.2129   41  157 15895.0485 7610.40923  52.1%   160   50s
H 4606  1556                    15895.048499 7925.13098  50.1%   162   53s
H 4637  1556                    15895.048488 7925.13098  50.1%   162   53s
  4839  1632 infeasible   57      15895.0485 8094.05239  49.1%   163   55s
  5855  1922     cutoff   32      15895.0485 8708.47547  45.2%   162   60s
  7064  2199 14712.8532   61  115 15895.0485 9618.00205  39.5%   166   65s
H 7360  2235                    15895.048482 9763.59617  38.6%   167   67s
  8262  2384 13405.5675   56  162 15895.0485 10278.0029  35.3%   169   71s
  9314  2517     cutoff   42      15895.0485 10891.9209  31.5%   172   75s
 10419  2695 15703.2113   65   99 15895.0485 11264.8002  29.1%   172   80s
 11643  2805 12712.6356   39   73 15895.0485 11517.9008  27.5%   174   86s
 12540  2893 14872.1679   78   85 15895.0485 11772.3504  25.9%   174   90s
 14321  2969 13492.5809   59  147 15895.0485 12128.8305  23.7%   174   96s
 15621  2992 13041.5460   61  168 15895.0485 12412.1406  21.9%   174  101s
 17386  2881     cutoff   49      15895.0485 12674.8277  20.3%   174  106s
 18660  2754 14758.4119   53  119 15895.0485 12942.8205  18.6%   174  112s
 19657  2524 15447.5172   53  153 15895.0485 13164.8743  17.2%   174  117s
 21185  2152 15246.0709   63   59 15895.0485 13525.3288  14.9%   174  123s
H21220  2152                    15895.048460 13535.1511  14.8%   174  123s
H21396  2152                    15895.048302 13559.7918  14.7%   174  123s
 21432  1838     cutoff   79      15895.0483 13632.2690  14.2%   174  126s
 23060   995 14932.1272   55  112 15895.0483 14070.4030  11.5%   173  131s
 25118    75     cutoff   43      15895.0483 15197.6490  4.39%   169  137s

Cutting planes:
  Gomory: 40
  Cover: 333
  Implied bound: 29
  MIR: 130
  Flow cover: 333
  Inf proof: 11
  Zero half: 22
  Mod-K: 1
  RLT: 420
  Relax-and-lift: 24

Explored 28472 nodes (4301975 simplex iterations) in 137.58 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 15895 15895 15895 ... 16062.5

Optimal solution found (tolerance 1.00e-04)
Best objective 1.589504830216e+04, best bound 1.589504830216e+04, gap 0.0000%

Solution

city_info = pd.DataFrame(autonomax.city_info())
city_info

	Index	Name	IsCoreCity	IsControlCenter	Demand	IngoingFlow	OutgoingFlow
0	0	Boden	False	False	4.5	4.000000e+00	8.500000
1	1	Borås	False	False	0.7	1.030000e+01	10.999999
2	2	Eskilstuna	False	False	1.1	2.520000e+01	26.300000
3	3	Falun	False	False	2.3	1.207923e-13	2.300000
4	4	Gävle	True	False	1.0	1.046000e+02	105.600000
5	5	Göteborg	False	False	5.8	6.652456e-13	5.800000
6	6	Halmstad	False	False	3.4	6.100000e+00	9.500000
7	7	Haparanda	False	False	4.6	0.000000e+00	4.600000
8	8	Helsingborg	False	False	2.2	3.900000e+00	6.100000
9	9	Hudiksvall	False	False	3.9	2.740000e+01	31.300000
10	10	Jönköping	False	False	1.2	1.243450e-12	1.200000
11	11	Kalmar	False	False	4.0	1.599999e+00	5.599999
12	12	Karlskrona	False	False	1.6	4.121148e-13	1.600000
13	13	Karlstad	False	False	0.9	1.229239e-12	0.900000
14	14	Kiruna	False	False	4.0	2.398523e-08	4.000000
15	15	Kristianstad	False	False	3.0	5.400125e-13	3.000000
16	16	Lidköping	False	False	1.2	1.340000e+01	14.600000
17	17	Linköping	False	False	4.1	7.400000e+00	11.500000
18	18	Luleå	False	False	2.0	1.310000e+01	15.100000
19	19	Malmö	False	False	2.6	1.300000e+00	3.900000
20	20	Motala	False	False	2.4	1.200000e+00	3.600000
21	21	Norrköping	False	False	1.7	1.890000e+01	20.599999
22	22	Nyköping	False	False	4.6	1.357137e-12	4.600000
23	23	Sandviken	True	False	4.5	1.079000e+02	112.400000
24	24	Skellefteå	False	False	4.4	1.510000e+01	19.499999
25	25	Skövde	False	False	2.5	1.100000e+01	13.500000
26	26	Stockholm	False	False	7.0	9.521273e-13	7.000000
27	27	Sundsvall	False	False	0.7	2.670000e+01	27.400000
28	28	Trelleborg	False	False	1.3	1.350031e-12	1.300000
29	29	Uddevalla	False	False	4.0	5.800000e+00	9.800000
30	30	Umeå	False	False	3.2	1.950000e+01	22.700000
31	31	Uppsala	True	False	1.9	7.140000e+01	73.299999
32	32	Varberg	False	False	0.8	9.500000e+00	10.300000
33	33	Vetlanda	False	False	2.1	5.300000e+00	7.400000
34	34	Vänersborg	False	False	3.6	9.800000e+00	13.400000
35	35	Västervik	False	False	1.8	5.599999e+00	7.399999
36	36	Västerås	True	True	1.4	1.754000e+02	64.399999
37	37	Växjö	False	False	2.3	3.000000e+00	5.300000
38	38	Örebro	False	False	4.1	3.260000e+01	36.700000
39	39	Örnsköldsvik	False	False	3.1	2.270000e+01	25.800000
40	40	Östersund	False	False	0.9	2.048473e-08	0.900000

edge_info = pd.DataFrame(autonomax.edge_info())
edge_info

	From	To	Type	Flow	Cost	Distance
0	Skellefteå	Umeå	SUB	19.499999	731.139133	111
1	Eskilstuna	Västerås	SUB	26.299999	244.508409	43
2	Gävle	Hudiksvall	SUB	-31.300000	1278.724564	118
3	Skövde	Örebro	SUB	13.500000	657.432752	132
4	Lidköping	Örebro	SUB	14.600000	841.276358	148
5	Linköping	Norrköping	SUB	11.500000	116.139455	44
6	Motala	Örebro	SUB	3.599999	110.457914	92
7	Boden	Kiruna	SUB	-3.999999	637.912777	291
8	Linköping	Vetlanda	SUB	-7.400000	389.358963	138
9	Sundsvall	Östersund	SUB	-0.899999	70.870141	166
10	Kalmar	Västervik	SUB	5.599999	300.208529	139
11	Halmstad	Helsingborg	SUB	-6.100000	145.447337	79
12	Umeå	Örnsköldsvik	SUB	22.700000	849.479945	111
13	Boden	Luleå	SUB	8.500000	58.656839	32
14	Karlstad	Örebro	SUB	0.900000	29.229970	77
15	Halmstad	Varberg	SUB	9.500000	167.432216	65
16	Lidköping	Vänersborg	SUB	-13.400000	206.938975	60
17	Uddevalla	Vänersborg	SUB	9.800000	39.823253	21
18	Gävle	Sandviken	CORE	105.600000	250.000000	25
19	Sandviken	Västerås	CORE	112.400000	1100.000000	110
20	Sundsvall	Örnsköldsvik	SUB	-25.800000	1177.683887	127
21	Jönköping	Motala	SUB	1.200000	52.590906	108
22	Eskilstuna	Nyköping	SUB	-4.600000	119.995513	83
23	Vetlanda	Växjö	SUB	-5.300000	112.394025	72
24	Borås	Varberg	SUB	-10.300000	260.758783	84
25	Haparanda	Luleå	SUB	4.600000	210.858567	124
26	Luleå	Skellefteå	SUB	15.099999	742.410208	133
27	Stockholm	Uppsala	SUB	7.000000	136.873708	69
28	Kristianstad	Växjö	SUB	2.999999	134.707628	120
29	Norrköping	Västervik	SUB	-7.399999	287.369478	112
30	Falun	Sandviken	SUB	2.299999	48.115159	65
31	Uppsala	Västerås	CORE	-64.399999	780.000000	78
32	Gävle	Uppsala	CORE	-73.299999	600.000000	60
33	Kalmar	Karlskrona	SUB	-1.599999	45.527155	79
34	Helsingborg	Malmö	SUB	-3.900000	67.318060	60
35	Göteborg	Uddevalla	SUB	5.800000	117.417503	70
36	Malmö	Trelleborg	SUB	-1.299999	18.150073	34
37	Västerås	Örebro	SUB	-36.699999	1050.855589	93
38	Hudiksvall	Sundsvall	SUB	-27.400000	641.652314	81
39	Eskilstuna	Norrköping	SUB	-20.599999	681.069442	102
40	Borås	Skövde	SUB	10.999999	384.262775	105

core_cost = sum(edge_info[edge_info['Type'] == 'CORE']['Cost'])
subnet_cost = sum(edge_info[edge_info['Type'] == 'SUB']['Cost'])
total_cost = core_cost + subnet_cost

print(f'core   = {core_cost:>9.3f}')
print(f'subnet = {subnet_cost:>9.3f}')
print('------------------')
print(f'total  = {total_cost:>9.3f}')
assert abs(autonomax.model.getObjective().getValue() - total_cost) < 1e-6

core   =  2730.000
subnet = 13165.048
------------------
total  = 15895.048

visualization.show(pd.DataFrame(autonomax.city_info()), pd.DataFrame(autonomax.edge_info()))